Lecture 3 Technologies Education 2015

1. Understanding
Computational Thinking
Technologies Education

2. 5:02

3. Systems Thinking
Computational Thinking
Design Thinking
Futures Thinking
Strategic Thinking

4. Systems Thinking
Computational Thinking
Project Based Learning

5. Systems Thinking
Computational Thinking
Understanding the problem to
be solved?

6. Systems Thinking
Computational Thinking
Thinking about the problem as
a system

7. Vehicle Example

8. Clothing Example

9. Systems Thinking
Stocks
Flows
Causal Loops
Flow Maps
Connection Circles
Simulations

10. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

11. Shelter Example

12. Garden Example

13. Systems Thinking
Computational Thinking
Other ways of thinking about
the problem and possible
solutions

14. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

15. Procedural Thinking

16. 0:52

17. Digital Technologies
Intellectually challenging and engaging problems remain to be
understood and solved. The problems and solutions are limited
only by our own curiosity and creativity

18. Computational Thinking

19. 3:36

20. Computational Thinking
The curriculum is designed so that students will develop and use
increasingly sophisticated computational thinking skills, and
processes, techniques and digital systems to create solutions to
address specific problems, opportunities or needs.

21. Computational Thinking
Computational thinking is a process of recognising aspects of
computation in the world and being able to think logically,
algorithmically, recursively and abstractly. Students will also
apply procedural techniques and processing skills when
creating, communicating and sharing ideas and information, and
managing projects.

22. 3:43

23. Key Concepts

24. Abstraction
Abstraction, which underpins all content, particularly the
content descriptions relating to the concepts of data
representation and specification, algorithms and
implementation

25. Abstraction
Abstraction involves hiding details of an idea, problem or
solution that are not relevant, to focus on a manageable number
of aspects. Abstraction is a natural part of communication:
people rarely communicate every detail, because many details
are not relevant in a given context. The idea of abstraction can be
acquired from an early age. For example, when students are
asked how to make toast for breakfast, they do not mention all
steps explicitly, assuming that the listener is an intelligent
implementer of the abstract instructions.

26. Abstraction
Central to managing the complexity of information systems is
the ability to ‘temporarily ignore’ the internal details of the
subcomponents of larger specifications, algorithms, systems or
interactions. In digital systems, everything must be broken down
into simple instructions.

27. Data collection,
representation and
interpretation
Data collection (properties, sources and collection of data), data
representation (symbolism and separation) and data
interpretation (patterns and contexts)

28. Data collection,
representation and
interpretation
The concepts that are about data, focus on the properties of data,
how they are collected and represented, and how they are
interpreted in context to produce information. These concepts in
Digital Technologies build on a corresponding Statistics and
Probability strand in the Mathematics curriculum.

29. Data collection,
representation and
interpretation
The Digital Technologies curriculum provides a deeper
understanding of the nature of data and their representation, and
computational skills for interpreting data. The data concepts
provide rich opportunities for authentic data exploration in other
learning areas while developing data processing and
visualisation skills.

30. Data collection,
representation and
interpretation
Data collection describes the numerical, categorical and textual
facts measured, collected or calculated as the basis for creating
information and its binary representation in digital systems.

31. Data collection,
representation and
interpretation
Data collection is addressed in the processes and production
skills strand. Data representation describes how data are
represented and structured symbolically for storage and
communication, by people and in digital systems, and is
addressed in the knowledge and understanding strand.

32. Data collection,
representation and
interpretation
Data interpretation describes the processes of extracting
meaning from data and is addressed in the processes and
production strand.

33. Specification (descriptions and techniques), algorithms
(following and describing) and implementation (translating and
programming)
Specification, algorithms and
implementation

34. Specification, algorithms and
implementation
The concepts specification, algorithms and implementation
focus on the precise definition and communication of problems
and their solutions. This begins with the description of tasks and
concludes in the accurate definition of computational problems
and their algorithmic solutions. This concept draws from logic,
algebra and the language of mathematics, and can be related to
the scientific method of recording experiments in science.

35. Specification, algorithms and
implementation
Specification describes the process of defining and
communicating a problem precisely and clearly. For example,
explaining the need to direct a robot to move in a particular way.

36. Specification, algorithms and
implementation
An algorithm is a precise description of the steps and decisions
needed to solve a problem. Algorithms will need to be tested
before the final solution can be implemented. Anyone who has
followed or given instructions, or navigated using directions, has
used an algorithm.

37. Specification, algorithms and
implementation
These generic skills can be developed without programming. For
example, students can follow the steps within a recipe or
describe directions to locate items. Implementation describes
the automation of an algorithm, typically by using appropriate
software or writing a computer program. These concepts are
addressed in the processes and production skills strand.

38. Digital systems
Digital systems (hardware, software, and networks and the
internet)

39. Digital systems
The digital systems concept focuses on the components of
digital systems: hardware and software (computer architecture
and the operating system), and networks and the internet
(wireless, mobile and wired networks and protocols).

40. Interactions and impacts
Interactions (people and digital systems, data and processes)
and impacts (sustainability and empowerment).

41. Interactions and impacts
The interactions and impacts concepts focus on all aspects of
human interaction with and through information systems, and
the enormous potential for positive and negative economic,
environmental and social impacts enabled by these systems.
Interactions and impacts are addressed in the processes and
production skills strand.

42. Interactions and impacts
Interactions refers to all human interactions with information
systems, especially user interfaces and experiences, and
human–human interactions including communication and
collaboration facilitated by digital systems. This concept also
addresses methods for protecting stored and communicated
data and information.

43. Interactions and impacts
Impacts describes analysing and predicting the extent to which
personal, economic, environmental and social needs are met
through existing and emerging digital technologies; and
appreciating the transformative potential of digital technologies
in people’s lives. It also involves consideration of the
relationship between information systems and society and in
particular the ethical and legal obligations of individuals and
organisations regarding ownership and privacy of data and
information.

44. Error Correction Example

45. Search Example

46. Travelling Salesman
http://www.cosc.canterbury.ac.nz/csfieldguide/dev/dev/ComplexityTractability.html

47. Computational Thinking
Abstraction
Data & Information Systems
Algorithms and Programming
Digital Systems
Implications and Impacts

48. 0:45

49. Griffith University
Dr Jason Zagami
www.zagami.info